This repository serves as a comprehensive, curated collection of resources for learning and utilizing OpenAI's Triton, a programming language designed for efficient GPU code generation. It targets developers, researchers, and engineers seeking to optimize deep learning workloads by writing custom GPU kernels, offering a structured path from basic concepts to advanced techniques and practical applications.

How It Works

The project is structured around a "Triton Day by Day" challenge, providing incremental learning through daily implementation tasks. These challenges start with fundamental GPU operations like vector addition and progress to more complex topics such as memory optimization, advanced indexing, multi-dimensional kernels, and reduction operations. Each kernel is accompanied by detailed explanations and benchmarking against standard PyTorch implementations to demonstrate performance gains and illustrate GPU programming principles.

Quick Start & Requirements

• Installation: No direct installation required; it's a resource repository.
• Prerequisites: Familiarity with Python, PyTorch, and GPU programming concepts is recommended. Access to an NVIDIA GPU is necessary for running the provided kernel examples and benchmarks.
• Resources: Links to official documentation, articles, videos, research papers, and community meetups are provided for in-depth learning.

Highlighted Details

• Daily Challenges: A structured, progressive learning path with daily Triton kernel implementation tasks.
• Benchmarking: Practical guidance on comparing custom Triton kernels against PyTorch implementations for performance analysis.
• Extensive Resource List: Curated links to articles, videos, research papers, and sample kernels covering a wide range of Triton applications and optimizations.
• Tooling: Highlights tools like Triton Deja-vu for autotune optimization, Triton Profiler, and Triton-util for enhanced development workflows.

Maintenance & Community

The repository encourages community contributions via pull requests and issues. Links to community meetups and discussions are provided to stay updated on Triton's latest developments.

Licensing & Compatibility

• License: MIT License.
• Compatibility: Suitable for commercial use and integration with closed-source projects.

Limitations & Caveats

This repository is a collection of resources and does not provide a runnable framework itself. Practical application requires setting up a development environment with appropriate NVIDIA drivers and CUDA toolkit versions.